Biowulf High Performance Computing at the NIH
cellranger on Biowulf

From the Cell Ranger manual:

Cell Ranger is a set of analysis pipelines that processes Chromium single cell 3’ RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. There are two pipelines:
  • cellranger mkfastq wraps Illumina's bcl2fastq to correctly demultiplex Chromium-prepared sequencing samples and to convert barcode and read data to FASTQ files.
  • cellranger count takes FASTQ files from cellranger mkfastq and performs alignment, filtering, and UMI counting. It uses the Chromium cellular barcodes to generate gene-cell matrices and perform clustering and gene expression analysis.
  • cellranger aggr aggregates results from cellranger count.
  • cellranger reanalyze takes feature-barcode matrices produced by cellranger count or aggr and re-runs the dimensionality reduction, clustering, and gene expression algorithms.

Note that the command line interface has changed since version 1.1.

These pipelines combine Chromium-specific algorithms with the widely used RNA-seq aligner STAR. Output is delivered in standard BAM, MEX, CSV, and HTML formats that are augmented with cellular information.
Important Notes

Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.

Allocate an interactive session and run the program. Sample session:

Copy the bcl format test data and run the demux pipeline

[user@biowulf]$ sinteractive --cpus-per-task=6 --mem=35g
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load cellranger
[user@cn3144 ~]$ cp $CELLRANGER_TEST_DATA/cellranger-tiny-bcl-1.2.0.tar.gz .
[user@cn3144 ~]$ cp $CELLRANGER_TEST_DATA/cellranger-tiny-bcl-samplesheet-1.2.0.csv .
[user@cn3144 ~]$ tar -xzf cellranger-tiny-bcl-1.2.0.tar.gz
[user@cn3144 ~]$ cellranger mkfastq --run=cellranger-tiny-bcl-1.2.0 \
                     --samplesheet=cellranger-tiny-bcl-samplesheet-1.2.0.csv \
                     --localcores=$SLURM_CPUS_PER_TASK \
cellranger mkfastq (1.2.1)
Copyright (c) 2016 10x Genomics, Inc.  All rights reserved.

Martian Runtime - 1.2.1 (2.1.2)
Running preflight checks (please wait)...
Checking run folder...
Checking RunInfo.xml...
Checking system environment...
Checking barcode whitelist...
Checking read specification...
Checking samplesheet specs...
2016-12-21 12:27:44 [runtime] (ready)           ID.H35KCBCXY.MAKE_FASTQS_CS.MAKE_FASTQS.PREPARE_SAMPLESHEET
- Run QC metrics:        /spin1/users/user/test_data/cellranger/H35KCBCXY/outs/qc_summary.json
- FASTQ output folder:   /spin1/users/user/test_data/cellranger/H35KCBCXY/outs/fastq_path
- Interop output folder: /spin1/users/user/test_data/cellranger/H35KCBCXY/outs/interop_path
- Input samplesheet:     /spin1/users/user/test_data/cellranger/H35KCBCXY/outs/input_samplesheet.csv

Pipestance completed successfully!

Note that it is necessary to specify --localcores and --localmem.

Cellranger may start an unreasonable number of processes or open too many files. If you encounter errors that include

... = os.fork()
OSError: [Errno 11] Resource temporarily unavailable 

or see unexpected results despite specifying --localcores and --localmem, you may have to raise the limit on the number of processes and/or open files allowed in your batch script:

[user@cn3144 ~]$ ulimit -u 10240 -n 16384
[user@cn3144 ~]$ cellranger mkfastq --run=cellranger-tiny-bcl-1.2.0 \
                     --samplesheet=cellranger-tiny-bcl-samplesheet-1.2.0.csv \
                     --localcores=$SLURM_CPUS_PER_TASK \

Generate counts per gene per cell

[user@cn3144 ~]$ cellranger count --id s1 \
                    --fastqs H35KCBCXY/outs/fastq_path \
                    --transcriptome=$CELLRANGER_REF300/GRCh38 \
                    --localcores=$SLURM_CPUS_PER_TASK \
                    --chemistry=SC3Pv2 \
cellranger count (1.2.1)
Copyright (c) 2016 10x Genomics, Inc.  All rights reserved.

Martian Runtime - 1.2.1 (2.1.2)
Running preflight checks (please wait)...
Checking sample info...
Checking FASTQ folder...
- Run summary HTML:                      /spin1/users/user/test_data/cellranger/s1/outs/web_summary.html
- Run summary CSV:                       /spin1/users/user/test_data/cellranger/s1/outs/metrics_summary.csv
- BAM:                                   /spin1/users/user/test_data/cellranger/s1/outs/possorted_genome_bam.bam
- BAM index:                             /spin1/users/user/test_data/cellranger/s1/outs/possorted_genome_bam.bam.bai
- Filtered gene-barcode matrices MEX:    /spin1/users/user/test_data/cellranger/s1/outs/filtered_gene_bc_matrices
- Filtered gene-barcode matrices HDF5:   /spin1/users/user/test_data/cellranger/s1/outs/filtered_gene_bc_matrices_h5.h5
- Unfiltered gene-barcode matrices MEX:  /spin1/users/user/test_data/cellranger/s1/outs/raw_gene_bc_matrices
- Unfiltered gene-barcode matrices HDF5: /spin1/users/user/test_data/cellranger/s1/outs/raw_gene_bc_matrices_h5.h5
- Secondary analysis output CSV:         /spin1/users/user/test_data/cellranger/s1/outs/analysis
- Per-molecule read information:         /spin1/users/user/test_data/cellranger/s1/outs/molecule_info.h5

Pipestance completed successfully!

Saving pipestance info to s1/s1.mri.tgz
node$ exit

The same job could also be run in cluster mode where pipeline tasks are submitted as batch jobs. This can be done by setting jobmode to slurm and limiting the max. number of concurrent jobs:

[user@cn3144 ~]$ cellranger count --id s1 \
                --fastqs H35KCBCXY/outs/fastq_path \
                --transcriptome=$CELLRANGER_REF300/GRCh38 \
                --chemistry=SC3Pv2 \
                --localcores=$SLURM_CPUS_PER_TASK \
                --localmem=34 \
                --jobmode=slurm --maxjobs=10

Don't forget to close the interactive session when done

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Though in the case of this small example this actually results in a longer overall runtime. Even when running in cluster mode, please run the main pipeline in an sinteractive session or as a batch job itself.

Batch job
Most jobs should be run as batch jobs.

Create a batch input file (e.g., which uses the input file ''. For example:

#! /bin/bash
module load cellranger || exit 1
## uncomment the following line if encountering 'resource unavailable' errors
## despite using --localcores and --localmem
# ulimit -u 4096
cellranger mkfastq --run=llranger-tiny-bcl-1.2.0 \
        --samplesheet=cellranger-tiny-bcl-samplesheet-1.2.0.csv \
        --localcores=$SLURM_CPUS_PER_TASK \
cellranger count --id s1 \
        --fastqs H35KCBCXY/outs/fastq_path \
        --transcriptome=$CELLRANGER_REF/GRCh38 \
        --localcores=$SLURM_CPUS_PER_TASK \
        --localmem=34 \
        --chemistry=SC3Pv2 \
        --jobmode=slurm --maxjobs=20

Again, please remember to include --localcoes and --localmem.

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=12 --mem=35g
Swarm of Jobs
A swarm of jobs is an easy way to submit a set of independent commands requiring identical resources.

Create a swarmfile (e.g. cellranger.swarm). For example:

cellranger mkfastq --run=./run1 --localcores=$SLURM_CPUS_PER_TASK --localmem=34
cellranger mkfastq --run=./run2 --localcores=$SLURM_CPUS_PER_TASK --localmem=34
cellranger mkfastq --run=./run3 --localcores=$SLURM_CPUS_PER_TASK --localmem=34

Submit this job using the swarm command.

swarm -f cellranger.swarm -g 35 -t 12 --module cellranger
-g # Number of Gigabytes of memory required for each process (1 line in the swarm command file)
-t # Number of threads/CPUs required for each process (1 line in the swarm command file).
--module cellranger Loads the cellranger module for each subjob in the swarm